{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of estimated health prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimated health prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All countries: Administrative costs\n",
    "countries = ['de','dk','fr','it','nl','se','sp','us']\n",
    "euro = ['de','dk','fr','it','nl','se','sp']\n",
    "stats = ['price','iota','hosp']\n",
    "table = pd.DataFrame(index=countries,columns=stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# European Countries and health market institutions\n",
    "statd  = ['price','comp','regu','quali','DemManag','pay']\n",
    "tabled = pd.DataFrame(index=euro,columns=statd) #countries[0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                us        de        dk        fr        it        nl  \\\n",
      "name                                                                   \n",
      "sigma     2.097183  2.097183  2.097183  2.097183  2.097183  2.097183   \n",
      "beta      0.970000  0.970000  0.970000  0.970000  0.970000  0.970000   \n",
      "phi       0.304140  0.304140  0.304140  0.304140  0.304140  0.304140   \n",
      "psi       0.168902  0.168902  0.168902  0.168902  0.168902  0.168902   \n",
      "delta_h1 -0.967380 -1.296156 -1.601068 -1.094451 -0.715889 -1.264525   \n",
      "delta_h2  3.487244  3.953905  4.273184  3.697940  3.867593  3.998342   \n",
      "eta       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "tfp       1.000000  1.008961  1.255176  0.923597  0.638877  1.009820   \n",
      "price     1.000000  0.846861  0.887615  0.614844  0.712935  0.698165   \n",
      "risk      0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
      "\n",
      "                se        sp  \n",
      "name                          \n",
      "sigma     2.097183  2.097183  \n",
      "beta      0.970000  0.970000  \n",
      "phi       0.304140  0.304140  \n",
      "psi       0.168902  0.168902  \n",
      "delta_h1 -1.525023 -0.005673  \n",
      "delta_h2  4.310607  3.394047  \n",
      "eta       0.000000  0.000000  \n",
      "tfp       0.788306  0.810865  \n",
      "price     0.843816  0.650287  \n",
      "risk      0.000000  0.000000  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('../estimation/output/params_ref_us.pkl')\n",
    "pars = df.loc[:,'value'].to_frame()\n",
    "pars.columns = ['us']\n",
    "for c in countries:\n",
    "    df = pd.read_pickle('../estimation/output/params_ref_'+c+'.pkl')\n",
    "    pars[c] = df.loc[:,'value']\n",
    "print(pars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_health = pars.loc['price',:]\n",
    "table.loc[:,'price']= price_health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tablep = table.sort_values('price',ascending=False)\n",
    "plt.figure()\n",
    "b = plt.bar(tablep.index,tablep['price'],color='grey')\n",
    "b[0].set_color('black')\n",
    "plt.ylabel('$p_g/p_{US}$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "AdmiCostH = np.array([ 9.00, np.nan, 8.77, np.nan, 10.85, np.nan, np.nan, 15.51]) \n",
    "table.loc[:,'hosp']= AdmiCostH/AdmiCostH[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 0.923157    intercept: -0.017949   R-squared: 0.604215\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "slopeH, interceptH, r_valueH, p_valueH, std_errH = stats.linregress(table.loc[['de','fr','nl','us'],'price'],table.loc[['de','fr','nl','us'],'hosp'])\n",
    "print(\"slope: %f    intercept: %f   R-squared: %f\" % (slopeH, interceptH,r_valueH**2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(table.loc[:,'price'],table.loc[:,'hosp'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(table.loc[['de','fr','nl','us'],'price'], interceptH + slopeH*table.loc[['de','fr','nl','us'],'price'], 'k')\n",
    "plt.ylim(0.45,1.05)\n",
    "plt.ylabel('Administrative Costs ($\\iota$)')\n",
    "plt.xlabel('health price')\n",
    "for x,y,z in zip(table.loc[:,'price'],table.loc[:,'hosp'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Health marlet institutions: European countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "typeReg = ['Physician payment',\n",
    "'Hospital payment',\n",
    "'Incentives for quality',\n",
    "'Choice among providers',\n",
    "'User choice of insurer',\n",
    "'Lever',\n",
    "'Regulation of physician supply',\n",
    "'Regulation of capital investment',\n",
    "'Regulation of price for physician services',\n",
    "'Regulation of price for hospital services',\n",
    "'Regulation of pharmaceutical price',\n",
    "'Regulation of prices charged to third-party',\n",
    "'Stringency of budget constraint',\n",
    "'Control of volume',\n",
    "'Gatekeeping',\n",
    "'Depth of basic insurance',\n",
    "'Definition of benefit basket',\n",
    "'Public health objectives',\n",
    "'Use of health technology assessment',\n",
    "'Degree of decentralisation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "RegInd = np.array([\n",
    "[4.5, 5.0, 0.0, 5.3, 6.0, 5.0, 3.6, 4.0, 3.0, 3.0, 1.0, 3.7, 2.0, 2.0, 3.0, 5.6, 4.0, 0.1, 3.6, 1.5],\n",
    "[2.5, 1.0, 0.5, 2.0, 1.0, 0.0, 4.8, 4.0, 4.0, 5.0, 1.0, 4.0, 2.0, 1.7, 6.0, 5.3, 2.5, 2.4, 6.0, 2.3],\n",
    "[4.5, 5.0, 0.5, 6.0, 2.0, 0.3, 4.8, 4.0, 4.0, 3.0, 5.0, 5.0, 2.0, 1.7, 3.0, 5.2, 5.0, 5.4, 4.0, 0.0],\n",
    "[1.0, 5.0, 1.0, 6.0, 0.0, 0.0, 4.8, 6.0, 3.0, 5.0, 5.0, 4.2, 5.0, 2.3, 6.0, 5.4, 5.0, 0.9, 3.6, 2.3],\n",
    "[5.5, 1.0, 1.5, 5.0, 4.0, 5.0, 4.8, 1.5, 2.0, 5.0, 5.0, 3.2, 2.0, 1.0, 6.0, 5.7, 5.0, 0.0, 6.0, 0.0],\n",
    "[0.5, 0.0, 1.0, 0.7, 1.0, 0.0, 3.6, 4.0, 4.0, 5.0, 5.0, 4.5, 2.0, 3.0, 6.0, 5.4, 5.0, 0.1, 4.7, 5.5],\n",
    "[0.0, 3.0, 1.2, 6.0, 0.0, 0.0, 2.4, 2.9, 4.0, 5.0, 5.0, 4.5, 6.0, 2.3, 0.0, 4.9, 2.5, 0.1, 4.0, 4.3],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tableR = pd.DataFrame(index=countries[0:7],columns=typeReg)\n",
    "\n",
    "for ii in range(20):\n",
    "    tableR.loc[:,typeReg[ii]] = RegInd[:,ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physician payment</th>\n",
       "      <th>Hospital payment</th>\n",
       "      <th>Incentives for quality</th>\n",
       "      <th>Choice among providers</th>\n",
       "      <th>User choice of insurer</th>\n",
       "      <th>Lever</th>\n",
       "      <th>Regulation of physician supply</th>\n",
       "      <th>Regulation of capital investment</th>\n",
       "      <th>Regulation of price for physician services</th>\n",
       "      <th>Regulation of price for hospital services</th>\n",
       "      <th>Regulation of pharmaceutical price</th>\n",
       "      <th>Regulation of prices charged to third-party</th>\n",
       "      <th>Stringency of budget constraint</th>\n",
       "      <th>Control of volume</th>\n",
       "      <th>Gatekeeping</th>\n",
       "      <th>Depth of basic insurance</th>\n",
       "      <th>Definition of benefit basket</th>\n",
       "      <th>Public health objectives</th>\n",
       "      <th>Use of health technology assessment</th>\n",
       "      <th>Degree of decentralisation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dk</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Physician payment  Hospital payment  Incentives for quality  \\\n",
       "de                4.5               5.0                     0.0   \n",
       "dk                2.5               1.0                     0.5   \n",
       "fr                4.5               5.0                     0.5   \n",
       "it                1.0               5.0                     1.0   \n",
       "nl                5.5               1.0                     1.5   \n",
       "se                0.5               0.0                     1.0   \n",
       "sp                0.0               3.0                     1.2   \n",
       "\n",
       "    Choice among providers  User choice of insurer  Lever  \\\n",
       "de                     5.3                     6.0    5.0   \n",
       "dk                     2.0                     1.0    0.0   \n",
       "fr                     6.0                     2.0    0.3   \n",
       "it                     6.0                     0.0    0.0   \n",
       "nl                     5.0                     4.0    5.0   \n",
       "se                     0.7                     1.0    0.0   \n",
       "sp                     6.0                     0.0    0.0   \n",
       "\n",
       "    Regulation of physician supply  Regulation of capital investment  \\\n",
       "de                             3.6                               4.0   \n",
       "dk                             4.8                               4.0   \n",
       "fr                             4.8                               4.0   \n",
       "it                             4.8                               6.0   \n",
       "nl                             4.8                               1.5   \n",
       "se                             3.6                               4.0   \n",
       "sp                             2.4                               2.9   \n",
       "\n",
       "    Regulation of price for physician services  \\\n",
       "de                                         3.0   \n",
       "dk                                         4.0   \n",
       "fr                                         4.0   \n",
       "it                                         3.0   \n",
       "nl                                         2.0   \n",
       "se                                         4.0   \n",
       "sp                                         4.0   \n",
       "\n",
       "    Regulation of price for hospital services  \\\n",
       "de                                        3.0   \n",
       "dk                                        5.0   \n",
       "fr                                        3.0   \n",
       "it                                        5.0   \n",
       "nl                                        5.0   \n",
       "se                                        5.0   \n",
       "sp                                        5.0   \n",
       "\n",
       "    Regulation of pharmaceutical price  \\\n",
       "de                                 1.0   \n",
       "dk                                 1.0   \n",
       "fr                                 5.0   \n",
       "it                                 5.0   \n",
       "nl                                 5.0   \n",
       "se                                 5.0   \n",
       "sp                                 5.0   \n",
       "\n",
       "    Regulation of prices charged to third-party  \\\n",
       "de                                          3.7   \n",
       "dk                                          4.0   \n",
       "fr                                          5.0   \n",
       "it                                          4.2   \n",
       "nl                                          3.2   \n",
       "se                                          4.5   \n",
       "sp                                          4.5   \n",
       "\n",
       "    Stringency of budget constraint  Control of volume  Gatekeeping  \\\n",
       "de                              2.0                2.0          3.0   \n",
       "dk                              2.0                1.7          6.0   \n",
       "fr                              2.0                1.7          3.0   \n",
       "it                              5.0                2.3          6.0   \n",
       "nl                              2.0                1.0          6.0   \n",
       "se                              2.0                3.0          6.0   \n",
       "sp                              6.0                2.3          0.0   \n",
       "\n",
       "    Depth of basic insurance  Definition of benefit basket  \\\n",
       "de                       5.6                           4.0   \n",
       "dk                       5.3                           2.5   \n",
       "fr                       5.2                           5.0   \n",
       "it                       5.4                           5.0   \n",
       "nl                       5.7                           5.0   \n",
       "se                       5.4                           5.0   \n",
       "sp                       4.9                           2.5   \n",
       "\n",
       "    Public health objectives  Use of health technology assessment  \\\n",
       "de                       0.1                                  3.6   \n",
       "dk                       2.4                                  6.0   \n",
       "fr                       5.4                                  4.0   \n",
       "it                       0.9                                  3.6   \n",
       "nl                       0.0                                  6.0   \n",
       "se                       0.1                                  4.7   \n",
       "sp                       0.1                                  4.0   \n",
       "\n",
       "    Degree of decentralisation  \n",
       "de                         1.5  \n",
       "dk                         2.3  \n",
       "fr                         0.0  \n",
       "it                         2.3  \n",
       "nl                         0.0  \n",
       "se                         5.5  \n",
       "sp                         4.3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tabled.loc[:,'price']  = price_health[euro]\n",
    "\n",
    "tabled.loc[:,'regu']   = np.sum(tableR.loc[:,[\n",
    "'Physician payment',\n",
    "'Hospital payment',\n",
    "'Regulation of physician supply',\n",
    "'Regulation of capital investment',\n",
    "'Regulation of price for physician services',\n",
    "'Regulation of price for hospital services',\n",
    "'Regulation of pharmaceutical price',\n",
    "'Regulation of prices charged to third-party'\n",
    "]],1)/8\n",
    "\n",
    "tabled.loc[:,'quali']  = tableR.loc[:,typeReg[2]]\n",
    "\n",
    "tabled.loc[:,'markup'] = np.array([1.01803, 0.675261, 0.310128, 0.464998, 0.372489, 0.561001,0.287985])\n",
    "\n",
    "tabled.loc[:,'DemManag'] = np.sum(tableR.loc[:,['Definition of benefit basket', 'Public health objectives']],1)/2\n",
    "\n",
    "tabled.loc[:,'pay'] = np.sum(tableR.loc[:,['Physician payment','Hospital payment']],1)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choice among providers\n",
    "comp3 = np.array([5.3, 2.0, 6.0, 6.0, 5.0, 0.7, 6.0])\n",
    "tabled.loc[:,'comp'] = comp3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>comp</th>\n",
       "      <th>regu</th>\n",
       "      <th>quali</th>\n",
       "      <th>DemManag</th>\n",
       "      <th>pay</th>\n",
       "      <th>markup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>0.846861</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.4750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>4.75</td>\n",
       "      <td>1.018030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dk</th>\n",
       "      <td>0.887615</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.2875</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.675261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fr</th>\n",
       "      <td>0.614844</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.4125</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.20</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.310128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>it</th>\n",
       "      <td>0.712935</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.464998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nl</th>\n",
       "      <td>0.698165</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.372489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se</th>\n",
       "      <td>0.843816</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.561001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp</th>\n",
       "      <td>0.650287</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.3500</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.287985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  comp    regu  quali  DemManag   pay    markup\n",
       "de  0.846861   5.3  3.4750    0.0      2.05  4.75  1.018030\n",
       "dk  0.887615   2.0  3.2875    0.5      2.45  1.75  0.675261\n",
       "fr  0.614844   6.0  4.4125    0.5      5.20  4.75  0.310128\n",
       "it  0.712935   6.0  4.2500    1.0      2.95  3.00  0.464998\n",
       "nl  0.698165   5.0  3.5000    1.5      2.50  3.25  0.372489\n",
       "se  0.843816   0.7  3.3250    1.0      2.55  0.25  0.561001\n",
       "sp  0.650287   6.0  3.3500    1.2      1.30  1.50  0.287985"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price and competition\n",
      "slope: -15.226401    p-value: 0.050218    intercept: 15.858211   R-squared: 0.568573\n",
      "price and regulation\n",
      "slope: -2.665627    p-value: 0.145061    intercept: 5.658085   R-squared: 0.373181\n",
      "price and quality controls\n",
      "slope: -2.062438    p-value: 0.327408    intercept: 2.362447   R-squared: 0.190599\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "print(\"price and competition\")\n",
    "slopec, interceptc, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'comp'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopec, p_value, interceptc,r_value**2))\n",
    "\n",
    "print(\"price and regulation\")\n",
    "sloper, interceptr, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'regu'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (sloper, p_value, interceptr,r_value**2))\n",
    "\n",
    "print(\"price and quality controls\")\n",
    "slopeq, interceptq, r_value, p_value, std_err = stats.linregress(tabled.loc[:,'price'], tabled.loc[:,'quali'])\n",
    "print(\"slope: %f    p-value: %f    intercept: %f   R-squared: %f\" % (slopeq, p_value, interceptq,r_value**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(tabled['price'],tabled['comp'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(tabled['price'], interceptc + slopec*tabled['price'], 'k')\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(0.25,7.5)\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "for x,y,z in zip(tabled['price'],tabled['comp'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tabled['price'],tabled['regu'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(tabled['price'], interceptr + sloper*tabled['price'], 'k')\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(3.2,4.5)\n",
    "for x,y,z in zip(tabled['price'],tabled['regu'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tabled['price'],tabled['quali'],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(tabled['price'], interceptq + slopeq*tabled['price'], 'k')\n",
    "plt.xlabel('health price')\n",
    "plt.ylabel('Index')\n",
    "plt.xlim(0.59,0.91)\n",
    "plt.ylim(-0.5,1.9)\n",
    "for x,y,z in zip(tabled['price'],tabled['quali'],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
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